KineticXGPU：暗扇区自散射的张量化碰撞算子
KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering
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中文总结 AI 辅助
提出基于PyTorch的张量化碰撞算子KineticXGPU，用于GPU加速暗扇区动量分布模拟，并研究弹性自相互作用对双峰分布的影响。
中文摘要 AI 辅助
在这项工作中，我们提出了KineticXGPU，一个基于PyTorch的$2\ o 2$弹性自碰撞算子实现，用于暗扇区动量分布。离散化的碰撞算子可以表示为张量收缩，因此非常适合GPU。作为一个应用，我们研究了一个双源冻结场景，其中最终分布可能发展出双峰形状。我们表明，增加弹性自相互作用的强度会逐渐消除这种结构，并将分布推向麦克斯韦-玻尔兹曼分布。我们将相空间公式与一组耦合数密度和速度色散的流体方程进行了比较。我们还比较了CPU和GPU的运行时间，并展示了张量化方法的计算优势。该代码已在GitHub上公开。
英文摘要
We present KineticXGPU, a PyTorch-based implementation of the \(2\to2\) elastic self-collision operator for dark-sector momentum distributions. The discretized collision operator can be expressed as tensor contractions and is therefore well suited for GPUs. As an application, we study a two-source freeze-in scenario in which the final distribution can develop a bimodal shape. We show that increasing the strength of elastic self-interactions progressively erases this structure and drives the distribution toward a Maxwell--Boltzmann distribution. We find that $2\to2$ self-interactions can reduce the warmness of nonthermal frozen-in dark matter, illustrating how they may alleviate structure-formation constraints in late-decay scenarios. We compare the phase-space formulation with a set of fluid equations that couple the number density and velocity dispersion. We also compare CPU and GPU runtimes and demonstrate the computational advantage of the tensorized approach. The code is publicly available on GitHub.
